Translational profiling of ET-insensitive cell populations in ER+ breast cancer reveals new therapeutic vulnerabilities
Endocrine therapy resistance in ER+ breast cancer remains a major therapeutic challenge in clinic. This study offers a robust workflow to identify ET-insensitive cell populations that persist under selective pressure of ET in clinical samples and experimental models. We demonstrated that these populations are transcriptionally diverse and not defined by ER status/activity or molecular subtype. Importantly, we developed a predictive therapeutic pipeline that enables identification of new drugs tailored to target ET-insensitive cell populations. These findings provide a clinically relevant strategy for overcoming ET resistance and support the integration of single-cell profiling and organoid modeling into precision oncology workflows.
import lamindb as ln
import spatialdata as sd
coll = ln.Collection.get(key="geo-gse310362")
coll.ordered_artifacts.df() # the 4 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()} Cite this collection
No linked publication found — please cite the data source.